The invention relates to a method and a device for analysing physiological measurement values of a user. Furthermore, the invention relates to a computer program with program code for carrying out a method according to the invention. Such devices, methods and computer programs can be used in general for acquiring and analysing physiological measurement data of a user, for example in long-term monitoring of human or animal users within the scope of so-called home monitoring or else during hospital stays. The method, the device and the computer program can be used in particular for automatically identifying patterns in a chronological sequence of physiological measurement values. In particular, the method, the device and the computer program can be used for automatically carrying out a retrospective consideration of current patterns in physiological measurement data, e.g., in glucose values or other types of analyte concentrations, by comparison with historical data. In particular, a historical situation of the user can be found in this case, which corresponds best to the current situation of the user in order, accordingly, to take suitable measures.
Further, the following disclosure relates generally to patient monitoring, and in particular to a continuous glucose monitoring system with an efficient pattern matching algorithm, a method, and a computer product thereof.
In general, diabetes can be characterized by hyperglycemia and relative insulin deficiency. There are two main types of diabetes, Type I diabetes (insulin-dependent diabetes mellitus) and Type II diabetes (non-insulin-dependent diabetes mellitus). In some instances, diabetes is also characterized by insulin resistance.
Insulin secretion functions to control the level of blood glucose to keep the glucose levels at an optimum level. Healthcare may involve both establishing a therapeutic program and monitoring the progress of the afflicted person. Monitoring blood glucose levels is an important process that is used to help diabetics maintain blood glucose levels as near as normal as possible throughout the day. Monitoring can also allow successful treatment of a diabetic by altering therapy as necessary. Monitoring may allow the diabetic to monitor more closely his or her condition and, in addition, can provide information of value to the healthcare provider in determining both progress of the patient and detecting any need to change the patient's therapy program.
Advances in the field of electronics over the past several years have brought about significant changes in medical diagnostic and monitoring equipment, including self-care monitoring. In controlling and monitoring diabetes, relatively inexpensive and easy-to-use blood glucose monitoring systems have become available that provide reliable information that allows a diabetic and his or her healthcare professional to establish, monitor and adjust a treatment plan.
There are two main types of blood glucose monitoring systems used by patients: single point (or non-continuous) systems and continuous systems. Non-continuous systems consist of meters and tests strips and require blood samples to be drawn from fingertips or alternate sites, such as forearms and legs. An example of a non-continuous system may require a diabetic to apply a blood sample to reagent-impregnated region of a test strip, wipe the blood sample from the test strip after a predetermined period of time, and, after a second predetermined period of time, determine blood glucose level by comparing the color of the reagent-impregnated regions of the test strip with a color chart supplied by the test strip manufacturer. These systems also can rely on lancing and manipulation of the fingers or alternate blood draw sites, which can be extremely painful and inconvenient, particularly for children.
An example of a continuous system is a continuous glucose monitor (“CGM”) that can be implanted subcutaneously and measure glucose levels in the interstitial fluid at various periods throughout the day, providing data that shows trends in glucose measurements over a period of time. CGMs can provide large quantities of data that need to be processed to find patterns of similar data. The data can be used to identify harmful patient behaviors or to help optimize therapy based on similar past experiences. It can also be used to monitor glucose over time to determine a blood sugar pattern. Because of the large quantities of data involved, an efficient algorithm may be needed to enable pattern matching on devices with limited processing power.
In addition to the so-called point measurements, which are only carried out once or a couple of times, the prior art has, inter alia, also disclosed long-term monitoring of one or more physiological parameters. In the following text, the invention will substantially be described with reference to physiological parameters in the form of analyte concentrations of one or more analytes in a bodily fluid of the user, e.g., a human or animal patient, independently of whether a disease is actually present or whether there should merely be monitoring of healthy users. Without restricting further possible applications, the invention will be described in the following text with reference to blood-glucose monitoring. However, in principle, the invention is also transferable to other types of analytes and/or monitoring other types of physiological parameters.
In recent times, a so-called continuous glucose measurement, which is also referred to as continuous monitoring (CM), in the interstitium of the user is becoming ever more established. This method is suitable for managing, monitoring and controlling, e.g., a diabetes status. By now, the prior art has in this case disclosed directly implanted electrochemical sensors, which are often also referred to as needle-type sensors (NTS). Here, the active sensor region is brought directly to a measurement location, which is generally arranged in the interstitial tissue and converts glucose into electric charge, for example by using an enzyme (e.g., glucose oxidase, GOD), which charge is proportional to the glucose concentration and can be used as a measurement variable. Examples of such transcutaneous measurement systems are described in U.S. Pat. No. 6,360,888 B1 or in US 2008/0242962 A1. Continuous monitoring systems generally acquire measurement values, e.g., glucose measurement values, at regular or irregular time intervals. By way of example, glucose measurement values can be acquired at intervals of 5 min or less in the case of implanted sensors.
In contrast to so-called point measurements, which merely acquire an instantaneous body state of the user, measurement data records of a long-term measurement of physiological parameters, such as, e.g., a long-term measurement of an analyte concentration in the body tissue, thus comprise a multiplicity of further items of information, which, in principle, are available for evaluation. In particular, it is possible to follow developments over time, follow the influences of external effects on the body of the user and maybe even propose likely future profiles of the measurement values and derive recommendations for the user from this. However, a technical challenge consists of the fact that the measurement data record reaches a technical time resolution that is confronted with a huge data volume and hence requires novel methods of data preparation, data aggregation and data reuse. Otherwise, the increase in the data volume can even lead to a reduction in the user-friendliness of the methods and devices for the user, and to a lacking overview for the treating medical practitioner.
EP 1 918 837 A1 has disclosed a method for processing a chronological sequence of measurement values of a time-dependent parameter. A method is described of how a patient can himself select relevant portions from a time profile of measurement values of the glucose concentration, which portions represent the isolated influence of individual events and make said profile transparent, comprehensible and predicable. The knowledge in respect of the metabolic state overall can be improved on the basis of a collection of such portions. To this end, the profile of a glucose concentration over time after an isolated event, e.g., a specific meal, is stored for later comparisons, for example under a reference corresponding to this meal. The patient can create a personal archive of such event-specific CM profiles for himself and use it for comparisons in respect of a current situation. This option of comparing is valuable to patients and treating medical practitioners for increasing the depth of knowledge in respect of personalized effects of meals, sport, travel, stress or hormonal states. Here, it is proposed to generate a portion from a curve profile by fixing a start time and an end time. This portion is assigned to a specific event, e.g., a specific meal, and is optionally stored under a specific reference that characterizes the event. If the patient is once again in a comparable situation, he can search his archive for corresponding previous events. Such a search is generally oriented towards the names, which were given by the patient himself. Possible finds can be compared to the current profile, and the patient can thus prepare for the current situation.
WO 2006/066585 A2 has also disclosed methods and devices for pattern recognition in physiological measurement data. Here, patterns are identified in a measurement data record, which patterns correspond to at least one physiological state of the user. Measurement values are stored in conjunction with user actions and allow a targeted search for patterns in the measurement data record in conjunction with thresholds for the user action.
Thus, the known methods in principle are very time-consuming and are possibly too complicated for potential users, in particular for children, elderly patients or patients with dementia. For example, the assignment of names to particular events, such as naming a specific meal, or a qualification and quantification of certain user events by the user himself is subject to very subjective criteria, and so, possibly, finding a corresponding pattern may not be possible or may even be misleading as a result of initial naming or storing that was not thought through by inexperienced or overburdened patients. A manual method is not efficient, particularly for large data stocks, as are already expected for a measurement period spanning a couple of days. Moreover, many methods presuppose smoothing of the generally noisy measurement value profiles. Furthermore, there are technical challenges in quantifying the similarity of portions. Moreover, manual methods in principle are time-consuming and generally inefficient. Furthermore, there has not yet been a satisfactory solution to technical challenges that occur in the processing of found patterns, particularly if a number of possible patterns have been identified.
Accordingly, it is an object of the present invention to specify a method, a computer program and a device that at least largely avoid the disadvantages of known methods, computer programs and devices. In particular, a method should be specified for analyzing physiological measurement values, which can easily be carried out online and in an automatic fashion, preferably in real-time, and which is able to find historical situations of the user that are as similar as possible to the current situation of the user in order to provide him with the option of reacting in an ideal fashion to the current situation. It should preferably be possible to establish a probable future profile of the physiological measurement values, and it should preferably be possible to specify boundary conditions that had a positive effect in similar situations in the past and which could also constitute expedient measures in a current situation.